PSYC 21621 Lecture Notes - Lecture 20: Variance, Standard Error, Test Statistic
16 Mar 2019
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Document Summary
Review hypothesis testing with z-scores: sample mean (m) estimates (& approximates) population mean ( , standard error describes how much difference is reasonable to expect between m and s. M: to test the hypothesis, we compare the obtained sample mean (m) wit the n hypothesized population mean ( ) by computing a z-score test statistic. Problems with z-scores: the z-score requires more information than researchers typically have available, requires knowledge of the population standard deviation, researchers usually have only the sample data available. Degrees of freedom: computation of sample variance requires computation of the sample mean first, only n-1 scores in a sample are independent, researchers call n-1 in the degrees of freedom, noted as df, df=n-1. Learning check: when n is small (less than 30) the t distribution is flatter and more spread out than the normal z distribution.
